Business ecosystems

Alternatives to Product Data Lake

Henrik Gabs LiliendahlLeave a comment

Within Product Information Management (PIM) there is a growing awareness about that sharing product information between trading partners is a very important issue.

So, how do we do that? We could do that, on a global scale, by using:

  • 1,234,567,890 spreadsheets
  • 2,345,678 customer data portals
  • 901,234 supplier data portals

Spreadsheets is the most common mean to exchange product information between trading partners today. The typical scenario is that a receiver of product information, being a downstream distributor, retailer or large end user, will have a spreadsheet for each product group that is sent to be filled by each supplier each time a new range of products is to be on-boarded (and potentially each time you need a new piece of information). As a provider of product information, being a manufacturer or upstream distributor, you will receive a different spreadsheet to be filled from each trading partner each time you are to deliver a new range of products (and potentially each time they need a new piece of information).

Customer data portals is a concept a provider of product information may have, plan to have or dream about. The idea is that each downstream trading partner can go to your customer data portal, structured in your way and format, when they need product information from you. Your trading partner will then only have to deal with your customer data portal – and the 1,234 other customer data portals in their supplier range.

Supplier data portals is a concept a receiver of product information may have, plan to have or dream about. The idea is that each upstream trading partner can go to your supplier data portal, structured in your way and format, when they have to deliver product information to you. Your trading partner will then only have to deal with your supplier data portal – and the 567 other supplier data portals in their business-to-business customer range.

Product Data Lake is the sound alternative to the above options. Hailstorms of spreadsheets does not work. If everyone has either a passive customer data portal or a passive supplier data portal, no one will exchange anything. The solution is that you as a provider of product information will push your data in your structure and format into Product Data Lake each time you have a new product or a new piece of product information. As a receiver you will set up pull requests, that will give you data in your structure and format each time you have a new range of products, need a new piece of information or each time your trading partner has a new piece of information.

Learn more about how that works in Product Data Lake Documentation and Data Governance.

alternatives
Potential number of solutions / degree of dissatisfaction / total cost of ownership

 

A System of Engagement for Business Ecosystems

Henrik Gabs LiliendahlLeave a comment

Master Data Management (MDM) is increasingly being about supporting systems of engagement in addition to the traditional role of supporting systems of record. This topic was first examined on this blog back in 2012 in the post called Social MDM and Systems of Engagement.

The best known systems of engagement are social networks where the leaders are Facebook for engagement with persons in the private sphere and LinkedIn for engagement with people working in or for one or several companies.

But what about engagement between companies? Though you can argue that all (soft) engagement is neither business-to-consumer (B2C) nor business-to-business (B2B) but human-to-human (H2H), there are some hard engagement going on between companies.

pdl-whyOne of the most important ones is exchange of product information between manufacturers, distributors, resellers and large end users of product information. And that is not going very well today. Either it is based on fluffy emailing of spreadsheets or using rigid data pools and portals. So there are definitely room for improvement here.

At Product Data Lake we have introduced a system of engagement for companies when it comes to the crucial task of exchanging product information between trading partners. Read more about that in the post What a PIM-2-PIM Solution Looks Like.

Is blockchain technology useful within MDM?

Henrik Gabs Liliendahl2 Comments

This question was raised on this blog back in January this year in the post Tough Questions About MDM.

Since then the use of the term blockchain has been used more and more in general and related to Master Data Management (MDM). As you know, we love new fancy terms in our else boring industry.

blockchainHowever, there are good reasons to consider using the blockchain approach when it comes to master data. A blockchain approach can be coined as centralized consensus, which can be seen as opposite to centralized registry. After the MDM discipline has been around for more than a decade, most practitioners agree that the single source of truth is not practically achievable within a given organization of a certain size. Moreover, in the age of business ecosystems, it will be even harder to achieve that between trading partners.

This way of thinking is at the backbone of the MDM venture called Product Data Lake I’m working with right now. Yes, we love buzzwords. As if cloud computing, social network thinking, big data architecture and preparing for Internet of Things wasn’t enough, we can add blockchain approach as a predicate too.

In Product Data Lake this approach is used to establish consensus about the information and digital assets related to a given product and each instance of that product (physical asset or thing) where it makes sense. If you are interested in how that develops, why not follow Product Data Lake on LinkedIn.

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Data Management Platforms for Business Ecosystems

Henrik Gabs LiliendahlLeave a comment

The importance of looking at your enterprise as a part of business ecosystems was recently stressed by Gartner, the analyst firm, as reported in an article with the very long title stating: Gartner Says CIOs Need to Take a Leadership Role in Creating a Business Ecosystem to Drive a Digital Platform Strategy.

In my eyes, this trend will have a huge impact on how data management platforms should be delivered in the future. Until now much of the methodology and technology for data management platforms have been limited to how these things are handled within the corporate walls. We will need a new breed of data management platforms build for business ecosystems.

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Such platforms will have the characteristics of other new approaches to handling data. They will resemble social networks where you request and accept connections. They will embrace data as big data and data lakes, where every purpose of data consumption are not cut in stone before collecting data. These platforms will predominately be based in the cloud.

Right now I am working with putting such a data management service up in the cloud. The aim is to support product data sharing for business ecosystems. I will welcome you, and your trading partners, as subscriber to the service. If you help trading partners with Product Information Management (PIM) there is a place for you as ambassador. Anyway, please start with following Product Data Lake on LinkedIn.

Adding Things to Product Data Lake

Henrik Gabs LiliendahlLeave a comment

Product Data Lake went live last month. Nevertheless, we are already planning the next big things in this cloud service for sharing product data. One of them is exactly things. Let me explain.

Product data is usually data about a product model, for example a certain brand and model of a pair of jeans, a certain brand and model of a drilling machine or a certain brand and model of a refrigerator. Handling product data on the model level within business ecosystems is hard enough and the initial reason of being for Product Data Lake.

stepping_stones_oc

However, we are increasingly required to handle data about each instance of a product model. Some use cases I have come across are:

  • Serialization, which is numbering and tracking of each physical product. We know that from having a serial number on our laptops and another example is how medicine packs now will be required to be serialized to prevent fraud as described in the post Spectre vs James Bond and the Unique Product Identifier.
  • Asset management. Asset is kind of the fourth domain in Master Data Management (MDM) besides party, product and location as touched in the post Where is the Asset. Also Gartner, the analyst firm, usually in theory (and also soon in practice in their magic quadrants) classifies product and asset together as thing opposite to party. Anyway, in asset management you handle each physical instance of the product model.
  • Internet of Things (IoT) is, according to Wikipedia, the internetworking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.

Fulfilling the promise of IoT, and the connected term Industry 4.0, certainly requires common understood master data from the product model over serialization and asset management as reported in the post Data Quality 3.0 as a stepping-stone on the path to Industry 4.0.

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Approaches to Sharing Product Information in Business Ecosystems

Henrik Gabs LiliendahlLeave a comment

One of the most promising aspects of digitalization is sharing information in business ecosystems. In the Master Data Management (MDM) realm, we will in my eyes see a dramatic increase in sharing product information between trading partners as touched in the post Data Quality 3.0 as a stepping-stone on the path to Industry 4.0.

Standardization (or standardisation)

A challenge in doing that is how we link the different ways of handling product information within each organization in business ecosystems. While everyone agrees that a common standard is the best answer we must on the other hand accept, that using a common standard for every kind of product and every piece of information needed is quite utopic. We haven’t even a common uniquely spelled term in English.

Also, we must foresee that one organization will mature in a different pace than another organisation in the same business ecosystem.

Product Data Lake

These observations are the reasons behind the launch of Product Data Lake. In Product Data Lake we encompass the use of (in prioritized order):

  • The same standard in the same version
  • The same standard in different versions
  • Different standards
  • No standards

In order to link the product information and the formats and structures at two trading partners, we support the following approaches:

  • Automation based on product information tagged with a standard as explained in the post Connecting Product Information.
  • Ambassadorship, which is a role taken by a product information professional, who collaborates with the upstream and downstream trading partner in linking the product information. Read more about becoming a Product Data Lake ambassador here.
  • Upstream responsibility. Here the upstream trading partner makes the linking in Product Data Lake.
  • Downstream responsibility. Here the downstream trading partner makes the linking in Product Data Lake.

cross-company-data-governanceData Governance

Regardless of the mix of the above approaches, you will need a cross company data governance framework to control the standards used and the rules that applies to the exchange of product information with your trading partners. Product Data Lake have established a partnership with one of the most recommended authorities in data governance: Nicola Askham – the Data Governance Coach.

For a quick overview please have a look at the Cross Company Data Governance Framework.

Please request more information here.

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Sharing Metadata

Henrik Gabs LiliendahlLeave a comment

In short, metadata is data about data. Handling metadata is an important facet of data management including in data governance, data quality management and Master Data Management (MDM). When it comes to the new trends in data management as big data and handling data in data lakes, the importance of metadata management will in my eyes become even more obvious.

In a current venture (Product Data Lake) we are working on building in metadata management for business ecosystems, meaning that trading partners can share product information either using the same metadata or linking their different metadata.

Using international, national and industry standards for product information will be the perfect solution within business ecosystem sharing of metadata and indeed this is the preferred option we support. However, there are many competing standards for product information and they come in developing versions, so having everyone on the same page at the same time is quite utopic.

Add to that everyone do not speak English – and even not the same variant of English. Metadata originates and should exist in the languages that is used in trading partnerships.

pdl-menu-manage-attributeIn Product Data Lake we have started out with these principles:

  • Product attributes can be tagged with an attribute type telling about what standard (if any) in terms of product identification, product classification or product feature it adheres to. More about that in the post Connecting Product Information.
  • Attribute short and long descriptions can be represented in different languages.
  • Trading partners can link their product attributes and have visibility in the Product Data Lake of the standards and descriptions used in the different languages they exist.

I will very much welcome your input to this quest and if you want to be involved please do not hesitate to be in touch with me here or on Xing, Viadeo or LinkedIn.

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Sign Up is Open

Henrik Gabs LiliendahlLeave a comment

Over the recent one and a half year many of the posts on this blog has been about Product Data Lake, a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information.

From my work as a data quality and Master Data Management (MDM) consultant, I have seen the need for a service to solve data quality issues, when it comes to product master data. My observation has been that the root cause of these issues are found in the way that trading partners exchange product information and digital assets.

It is the aim of Product Data Lake to ensure:

  • Completeness of product information by enabling trading partners to exchange product data in a uniform way
  • Timeliness of product information by connecting trading partners in a process driven way
  • Conformity of product information by encompassing various international standards for product information
  • Consistency of product information by allowing upstream trading partners and downstream trading partners to interact with in-house structure of product information
  • Accuracy of product information by ensuring transparency of product information across the supply chain.

You can learn more about how Product Data Lake works on the documentation site.

pdl-how-much-smallBecome a:

Sign Up is open on www.productdatalake.com

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Data Quality 3.0 as a stepping-stone on the path to Industry 4.0

Henrik Gabs LiliendahlLeave a comment

The title of this blog post is a topic on my international keynote at the Stammdaten Management Forum 2016 in Düsseldorf, Germany on the 8th November 2016. You can see the agenda for this conference that starts on the 7th and end the on 9th here.

stepping_stones_ocData Quality 3.0 is a term I have used over the years here on the blog to describe how I see data quality, along with other disciplines within data management, changing. This change is about going from focusing on internal data stores and cleansing within them to focusing on external sharing of data and using your business ecosystem and third party data to drastically speed up data quality improvement.

Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies. When we talk about big data most will agree that success with big data exploitation hinges on proper data quality within master data management. In my eyes, the same can be said about success with industry 4.0. The data exchange that is the foundation of automation must be secured by common understood master data.

So this is the promising way forward: By using data exchange in business ecosystems you improve data quality of master data. This improved master data ensures the successful data exchange within industry 4.0.

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Ways of Sharing Product Data in Business Ecosystems

Henrik Gabs LiliendahlLeave a comment

Sharing product data within business ecosystems of manufacturers, distributors, retailers and end users has grown dramatically during the last years driven by the increased use of e-commerce and other customer self-service sales approaches.

At Product Data Lake we recently had a survey about how companies shares product data today. The figures were as seen below:

our survey

The result shows that there are different approaches out there. Spreadsheets still rules the world though closely, in this survey, followed by external data portals. Direct system to system approaches are also present while supplier portals seems to be not that common.

At the Product Data Lake we aim to embrace those different approaches. Well, regarding use of spreadsheets and digital asset files via eMail our embracement is meant to be that of a constrictor snake. The Product Data Lake is the solution to end the hailstorms of spreadsheets with product data within cross company supply chains.

For external data portals, the Product Data Lake offers the concept of a data reservoir. A data reservoir in the Product Data Lake can be with an industry focus or with a special focus on certain data elements as for example sustainability data as described in the post Sustainability Data in PIM.

Direct systems to system exchange can be orchestrated through the Product Data Lake and supplier portals can served by the Product Data Lake. In that way existing investments in those approaches, that typically are implemented to serve basic data elements shared with your top trading partners, can be supplemented by a method that caters for exchange with all your trading partners and covering all data elements and digital assets.

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